Over recent years, for example, to achieve safety in public places and facilities such as airports and the like and to ensure information security and the like upon logging into an information processing system and the like, pattern verification for confirming a person, for example, using biological information is performed.
The biological information refers to a physical feature such as a face, a fingerprint, and the like. The pattern refers to image data of a face, a fingerprint, and the like.
In other words, in pattern verification, for example, a face image or a fingerprint image is input as a pattern and then the pattern is determined whether to be the same as a pattern of an image previously registered.
In general, such a technique for verifying patterns calculates a similarity indicating to what extent features in two input patterns are similar and compares the similarity with a predetermined threshold for verification determination.
FIG. 8 illustrates a configuration example of such a method. FIG. 8 is a block diagram illustrating a function in a common verification device 200.
The common verification device 200 illustrated in FIG. 8 includes a feature extraction unit 201 and a feature extraction unit 202 extracting features related to two input patterns. The verification device 200 includes a similarity calculation unit 203 calculating a similarity between the features extracted in the feature extraction units 201 and 202 and a verification determination unit 204 verifying the two input patterns based on the calculated similarity.
When an input pattern x 206 and an input pattern y 207 are input, the common verification device 200 illustrated in FIG. 8 extracts features of respective patterns in the feature extraction units 201 and 202.
The common verification device 200 calculates similarities in the respective features extracted from the input pattern x 206 and the input pattern y 207 in the similarity calculation unit 203. The verification device 200 determines a similarity degree between the input patterns in the verification determination unit 204 based on the similarities calculated in the similarity calculation unit 203 to output a verification result 205.
In such a common verification device, a method how to calculate similarities from features in input patterns affects verification performance (i.e., verification accuracy upon verification, and time and cost necessary upon verification) to a large extent.
Over recent years, as one of methods commonly used, there is known a method for calculating a similarity between input patterns based on a distance between feature vectors extracted from the input patterns. Such a method performs a determination as the same pattern when a calculated similarity is higher than a predetermined threshold (i.e., a distance between feature vectors is small), and performs a determination as different patterns when the similarity is lower than the predetermined threshold (i.e., the distance between the feature vectors is long), for example.
The feature vector represents a set of numerical values obtained by quantifying and arranging features in an input pattern according to feature amounts thereof. The distance between feature vectors represents a distance in sets of numerical values in a feature space.
One of problems making such pattern verification difficult lies in changes in a photographic condition between input patters. An ideal target is that, for example, upon performing face verification, when a reference pattern is a person's own pattern, a high similarity is obtained due to a similar face image, and when the reference pattern is another person's pattern, a low similarity is obtained due to a different face image.
However, actually, upon inputting patterns, when face postures or photographic conditions (photographic environments) such as lighting and the like are different between face images to be input, similarity decreases due to images different in appearance (i.e., feature vectors are different) even when a reference pattern is a person's own pattern, resulting in a problem in which the person him-/her-self is falsely determined as another person.
The above problem results from direct use of a similarity between input patterns for verification determination. Therefore, a method for using a plurality of similarities between those other than input patterns in addition to a similarity between the input patterns has been proposed. As such a method, Zero Normalization (hereinafter, abbreviated as “Z-norm”) is widely known. FIG. 6 illustrates a configuration example of this method.
FIG. 6 is a block diagram illustrating a function in a common verification device 300 using a Z-norm method.
The common verification device 300 using a Z-norm method illustrated in FIG. 6 includes an input pattern x 307, a verification pattern storage unit 301, a feature extraction unit 302, a similarity calculation unit 303, a similarities integration unit 304, and a verification determination unit 305.
The verification pattern storage unit 301 in such a common verification device 300 stores a plurality of verification patterns (i.e., corresponding to the above reference pattern, and hereinafter, being the same in description regarding the present common example). The feature extraction unit 302 extracts a feature of the input pattern x 307 and features of several verification patterns picked up from the verification pattern storage unit 301.
The similarity calculation unit 303 in the common verification device 300 calculates similarities in the extracted features. The similarities integration unit 304 compares the calculated similarities. The verification determination unit 305 determines similarity degrees of the compared results to perform verification.
When the input pattern x 307 intended to be verified is input, the common verification device 300 using a Z-norm method illustrated in FIG. 6 picks up several ones from a plurality of verification patterns stored on the verification pattern storage unit 301 and extracts respective features together with the input pattern x 307 in the feature extraction unit 302.
The common verification device 300 calculates respective similarities in the features of the verification patterns with respect to a certain feature of the input pattern in the similarity calculation unit 303 and compares similarities in the verification patterns with respect to the input pattern over the several verification patterns picked up in the similarities integration unit 304. The verification device 300 outputs a verification result 306 by determining similarity degrees in the verification determination unit 305 based on the comparison result.
In the Z-norm method, distribution of similarities between each of verification patterns to be references upon verification registered in a data base (corresponding to the verification pattern storage unit 301 in FIG. 6) and an input pattern intended to be verified is normalized into a normal distribution to perform a similarity correction.
Thereby, even when, for example, a similarity between person's own face images decreases due to a difference in photographic condition in input patterns, a distribution of similarities to another person also decreases totally in the same manner and therefore, a correction using Z-norm achieves similarity enhancement and then false verification is unlikely to occur.
PTL 1 to PTL 3 employ a method for correcting the same similarity between input patterns as in Z-norm using similarities to a plurality of other patterns registered.
On the other hand, in PTL 4, a plurality of three-dimensional models are previously prepared; an image (hereinafter, referred to as a “comparative pattern” in description regarding the present common example) in a posture close to an input pattern and a lighting condition is generated for each model; and a similarity between an input pattern and a comparative pattern is used as a feature amount to perform a verification.
Such a method uses no similarity between input patterns and therefore, is independent of photographic conditions upon photographing the input patterns. However, the method has a problem in which it is necessary to estimate a photographic condition of the input pattern to generate the comparative pattern.
FIG. 7 illustrates a configuration example in this method. FIG. 7 is a block diagram illustrating a function in a common verification device 400 using a comparative pattern generation unit.
The common verification device 400 using a comparative pattern generation unit illustrated in FIG. 7 includes an input pattern x 410 and an input pattern y 411, a model storage unit 401, a comparative pattern generation unit 402 and a comparative pattern generation unit 403, a feature extraction unit 404 and a feature extraction unit 405, a similarity calculation unit 406 and a similarity calculation unit 407, and a verification determination unit 408.
The model storage unit 401 in the common verification device 400 using a comparative pattern generation unit illustrated in FIG. 7 stores models to be bases of comparative patterns to be generated. The comparative pattern generation unit 402 and the comparative pattern generation unit 403 generate comparative patterns by estimating the comparative patterns on the basis of the models. The feature extraction unit 404 and the feature extraction unit 405 extract respective features related to the input patterns and the comparative patterns.
The similarity calculation unit 406 and the similarity calculation unit 407 in such a common verification device 400 calculate similarities in features extracted in the feature extraction units 404 and 405, respectively. The verification determination unit 408 compares similarity degrees of the similarities calculated in the similarity calculation unit 406 and the similarity calculation unit 407.
When the input pattern x 410 and the input pattern y 411 are input, the common verification device 400 using a comparative pattern generation unit illustrated in FIG. 7 generates comparative patterns in the comparative pattern generation units 402 and 403 on the basis of the models that are bases of the comparative patterns stored on the model storage unit 401.
The comparative patterns generated in the comparative pattern generation units 402 and 403 are input to the feature extraction units 404 and 405 together with the input pattern x 410 and the input pattern y 411, respectively, to extract respective features.
The common verification device 400 using a comparative pattern generation unit calculates respective similarities to the comparative patterns for the two input patterns in the similarity calculation units 406 and 407 and determines similarity degrees in the verification determination unit 408 based on the calculated similarities to output a verification result 409.